Identification of key regulatory molecules in the early development stage of Alzheimer's disease

Abstract Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, the incidence of which increases with age, and the pathological changes in the brain are irreversible. Recent studies have highlighted the essential role of long noncoding RNAs (lncRNAs) in AD by acting as competing endogenous RNAs (ceRNAs). Our aim was to construct lncRNA‐associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD. AD related datasets come from AlzData and GEO databases. The R package ‘Limma’ identifies differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases for functional enrichment analysis. Protein–protein interactions (PPIs) in DEGs were constructed in the STRING database, and Cytoscape software identified DEGs. Convergent functional genomics (CFG) analysis of differentially expressed hub genes (referred to as early‐DEGs) in the brain before the development of AD pathology. The AlzData database analyses the expression levels of early‐DEGs in different nerve cells. The lncRNA‐miRNA‐mRNA regulatory network was established according to the ceRNA hypothesis. We identified four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa‐let‐7c‐5p, hsa‐miR‐107, hsa‐miR‐129‐2‐3p and hsa‐miR‐214‐3p) were preliminarily identified as potential biomarkers for early AD, competitively regulating Atp6v0b, Atp6v1e1 Atp6v1f and Syt1. This study indicates that NEAT1, XIST, HCG18 and KCNQ1OT1 act as ceRNAs in competitive binding with miRNAs to regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 before the occurrence of pathological changes in AD.

effective drugs to cure this disease. 5By the time a clinical diagnosis of AD is established, neurons in many brain regions have already undergone substantial changes.At the early stage of the disease, patients do not usually have symptoms (such as cognitive or functional decline), but pathological changes have already occurred in the nervous system.By the time of appearance of clinical manifestations of AD, the quality of life of patients and their families is severely impacted.Therefore, identifying effective biomarkers is critical to uncover the underlying mechanisms and facilitate clinical diagnosis and treatment of AD.
Long noncoding RNAs (lncRNAs) refer to RNAs exceeding 200 nucleotides (nt) in length that do not code for proteins.It is estimated that lncRNAs are more numerous than protein-coding genes in humans. 6e development of more advanced RNA sequencing technologies has catalysed the advancement of epigenomics and computational prediction techniques. 7,8Studies have demonstrated the involvement of lncRNAs in the causation of human neurological diseases such as schizophrenia, autism spectrum disorder (ASD), Parkinson's disease (PD), Huntington's disease (HD) and AD. 9 Micro RNAs (miRNAs) are single-stranded RNAs that act on the partially complementary site of the 3′-untranslated region (UTR) of the target mRNA through the base pairing process and negatively regulate gene expression, resulting in translational inhibition or attenuation of the target gene. 102][13] lncRNAs are also a type of ceRNAs involved in this process. 14Studies have consistently reported the link between lncRNAs and AD.Among them, BACE1-as is the most widely studied lncRNA.The expression level of BACE1 in plasma was identified as a potential biomarker of brain amyloidosis in AD patients. 15,16CE1-as was shown to upregulate BACE1 mRNA and protein expression levels by preventing miRNA-485-5p from binding to the open reading frame of BACE1, 17 resulting in enhanced Aβ amyloid formation and the reduced protective effects miR-132-3p overpression on synaptic plasticity.18 BC200 is a lncRNA that regulates dendritic neuroprotein synthesis and synaptic plasticity by targeting eukaryotic initiation factor 4A (eIF4A).19 Another lncRNA closely related to AD is E230001N04Rik, which was found to regulate the production of tau aggregates in an in vitro AD model.20 Even though many recent studies have demonstarted association of several lncRNAs with AD, the regulatory role of lncRNAs as ceRNAs in AD is not well characterized and needs further exploration.
Owing to the potential role of lncRNAs in the diagnosis and treatment of AD, we used the AlzData database and GEO database for mining analysis of genes associated with AD.By convergent functional genomics (CFG) ranking and analysis of differentially expressed genes (DEGs), early genes significantly associated with AD before overt pathological changes were identified in different brain regions.Subsequently, functional annotation and enrichment analysis were performed.We identified miRNAs and lncRNAs that could bind these genes.A lncRNA-related ceRNA regulatory network composed of AD potential biomarker genes was constructed based on the ceRNA hypothesis.Our aim was to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD development.

| Function enrichment and protein-protein interaction (PPI) network analysis
We employed Metascape (https:// metas cape.org) to obtain information on DEGs in different brain regions for Gene Ontology (GO) analysis, including the involved biological processes (BP), cellular components (CC) and molecular functions (MF).Potential functions analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.p < 0.05 was set as the cutoff criteria and the plots were constructed using an online bioinformatics application (http:// www.bioin forma tics.com.cn).Using Cytoscape (version 3.6.0),we visualized the PPI network for DEGs in different brain regions using the STRING database.

| Gene cluster identification and enriched pathway and biological processes of hub genes
MCODE in Cytoscape was employed to identify remarkable clusters in the PPI network.We selected cluster 1 of each PPI network in different brain regions as the hub genes.Then, KEGG enrichment pathways and BP of hub genes for each brain region were obtained through Metascape and visualized through bioinformatics tools.

| Analysis of hub genes related to early-DEGs of AD
In the Alzdata database, a CFG was conducted on hub genes that were differentially expressed in AD mouse models prior to the development of overt AD pathology (referred to as early-DEGs).These early-DEGs were visualized in Cytoscape (version 3.6.0)software.Then, Metscape was used to further analyse the relevant pathways and biological processes of early-DEGs.

| Prediction of pivotal miRNAs and construction of miRNA-gene interaction networks
The miRNAs related to the crucial pathways targeted by Early-DEGs were selected by miRWalk 2.0.The three databases of miRWalk, miR-Net and miRTarBase were used to verify the accuracy of the intersection results.The final results were processed with Cytoscape after obtaining the intersection and identifying miRNAs that targeted more than two genes for further analysis.

F I G U R E 1
Flowchart illustrating the study design.

| Identification of DEGs in different brain regions
We identified DEGs in different brain regions of AD patients and healthy individuals by downloading cross-platform normalized expression data from the Alzdata database, including a total of 13 GEO datasets (GSE12685, GSE36980, GSE48350, GSE5281, GSE53890, GSE66333, GSE15222, GSE28146, GSE29378, GSE29652, GSE3726, GSE26927 and GSE26972).Among them, 716 genes (243 up-regulated genes and 473 down-regulated genes) for EC, 311 genes (137 up-regulated genes and 174 down-regulated genes) for HP, 1600 genes (568 up-regulated genes and 1032 down-regulated genes) for TC and 242 genes (148 up-regulated genes and 94 down-regulated genes) for FC were identified as DEGs (Table 2).Sangerbox and bioinformatics results were visualized with volcano plots (Figure 2A-D) and heatmaps (Figure 2E-H) according to gene expression analysis.OmicStudio tool (https:// www.omstu dio.cn/ tool) was used to obtain intersecting DEGs of the 13 datasets, leading to the identification of 27 DEGs for each brain region (Figure 2I).

| Functional enrichment analysis of DEGs
Metascape was used to analyse GO terms and KEGG pathway enrichment of DEGs in four different brain regions (cutoff value p < 0.05).GO term enrichment analysis verified that DEGs in EC and FC brain regions were primarily related to the components of the neuronal body (Figure 2J,M), while those in the HP region were mostly associated with transport vesicles and chemical synaptic transmission (Figure 2K).DEGs in the TC region were mostly related to the component of the major axon (Figure 2L).The most important pathway of DEGs in EC, HP and TC brain regions revealed by KEGG enrichment analysis was synaptic vesicle cycle (Figure 2N-P), while genes in FC brain region were mainly involved in mineral absorption (Figure 2Q).

| PPI network construction and hub genes analysis in four brain regions
Using the STRING database, a PPI network was visualized for DEGs in different brain regions using Cytoscape.MCODE in Cytoscape was used to identify remarkable clusters in this PPI network.We selected cluster 1 in each PPI network of different brain regions as hub genes and screened out 41 genes in the EC region, 29 genes in the HP region, 12 genes in the TC region and 11 genes in the FC region for inclusion in the PPI network of the DEGs (Table 3, Figure 3A-D).

| Biological process analysis and crucial pathway verification of hub genes
Functional enrichment analysis of significant pathways and biological processes was performed on hub genes in 4 brain regions by Metascape (p < 0.05) (Tables S1 and S2).The results of BP enrichment revealed that the hub genes in the EC region were primarily involved in regulating exocytosis, and those in the HP region were mainly associated with chemical synaptic transmission.The hub genes in the TC region and FC region were involved in cell chemotaxis and post-translation protein modification (Figure 3E).
Besides, KEGG pathway enrichment of hub genes in EC region demonstrated the pathway that was primarily correlated to synaptic vesicle cycle.HP region was chiefly related to GABAergic synaptic pathway.In contrast, TC and FC regions were related to chemokine signalling and the PI3K-Akt signalling pathway, respectively (Figure 3F).

| CFG, biological process and pathway analysis of early-DEGs in AD
Hub genes were selected from cluster 1 of each PPI network involved in different brain regions (Figure 4A).To further testify the association of chosen hub genes with AD, CFG rank in Alzdata was used to analyse the 29 hub genes differentially expressed in the   AD mouse model before the occurrence of AD pathology (early-DEGs) and these early-DEGs were visualized in Cytoscape software (Figure 4B).It was possible to effectively comprehend whether the expression level of hub genes was regulated by AD genetic variation.
Hub genes associated with APP, PSEN1, PSEN2, APOE or MAPT interactions were significantly involved in AD pathology in Aβ or tau line AD mouse models.The results of CFG rank revealed that two genes (GABRG2, GNG3) in EC region, three genes (GABRG2, GNG3, SH3GL2) in HP region, two genes (AGT, CXCL16) in TC region and 1 gene in FC region (C3) were related with AD, among which GABRG2, CXCL16 and C3 were strongly correlated with Aβ and tau (Table S3).
Moreover, the BP involved in hub genes was predominantly enriched in phagosome acidification and proton transmembrane transport (Figure 4C).Pathway enrichment results exhibited that the synaptic vesicle cycle was the dominant pathway concerned with hub genes (Figure 4D).

| Further exploration of hub genes correlated to early-DEGs of AD
Based on the results of pathway enrichment, seven genes (Atp6v0b, Atp6v0c, Atp6v0d1, Atp6v1e1, Atp6v1f, Slc17a7 and Syt1) associated with the synaptic vesicle cycle were selected in each of the four brain regions (EC, HP, TC and FC) (Table 4).We examined the expression levels of hub genes by using the Alzdata.Only ATP6V1E, Slc17a7 and Syt1 were significantly differentially expressed (Figure 4H,J,K).

| Construction of ceRNA network analysis of early-DEGs of AD
We predicted miRNA that targeted hub genes related to the synaptic vesicle cycle by performing miRWalk 2.0 (http:// zmf.umm.

| DISCUSS ION
By the time AD is diagnosed clinically, significant changes have already occurred in neurons across multiple brain regions.In recent years, many studies have reported the mechanism of delaying the occurrence and development of neurodegenerative diseases.During cellular stress response, the cytoprotective genes of vitagenes produce molecules with antioxidant and anti-apoptotic activities such as heme oxygenase-1.4][35] Concetta et al. also found that curcumin and 3,4-dihydroxyphenylacetaldehyde (DOPAL) can also exert neuroprotective effects through the vitagenes system. 36,37However, in the early stages of the disease, patients usually have no symptoms, but pathological changes have occurred in the nervous system.
Therefore, identifying effective early biomarkers is very important to promote the clinical diagnosis and treatment of AD.In the classic symptom classification of AD, mild cognitive impairment (MCI) serves as a transitional state between normal ageing and AD.Research by Casati et al. found that TREM2 can be used as a peripheral biomarker for early AD (MCI). 38In addition, proteins such as NEL-like protein 1, human kallikrein 14 and centrin-2 can be detected in the early stages of AD and appear throughout the disease progression. 39This study constructed an interrelated ceRNA regulatory network by applying bioinformatics methods to investigate the key lncRNAs and miRNAs interacting with early-DEGs of AD.The objective was to elucidate the potential early molecular and biological targets based on this ceRNA network.Four genes (Atp6v0b, Atp6v1e1, Atp6v1f and Syt1) were identified based on the bioinformatics analysis.They were significantly associated with the synaptic vesicle cycle during the early stages of the development of AD.Based on this finding, we discovered four lncRNAs (HCG18, KCNQ1OT1, NEAT1 and XIST) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) and jointly constructed a ceRNA network.These markers provide new ideas for preventing the occurrence and development of AD in the early stages.
Our bioinformatics results suggest that the expression of Syt1 was decreased in the four brain regions in AD patients.This is consistent with the previously reported downregulation of Syt1 in multiple brain regions of AD patients. 40,41SYT is an integral membrane protein of synaptic vesicles and is believed to act as a sensor of calcium ions (Ca 2+ ) during vesicle trafficking and exocytosis.[46][47][48][49] Our results found that X-inactivity specific transcript (XIST) can also target hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p to affect the expressions of Syt1, Atp6v0b, Atp6v1e1 and Atp6v1f.Several recent studies have reported the involvement of lncRNAs as ceRNAs in the pathogenesis of AD.XIST has been suggested to be mainly responsible for the inactivation of X chromosome and likely plays a role in the prevention of cerebral ischemic injury. 50,51Furthermore, Wang et al. reported significant up-regulation of lncRNA XIST in AD brain and that XIST induced the toxicity of Aβ to hippocampal neurons by targeting miR-132. 52This is further supplemented by the 4 miRNAs identified in the present study.Our results are also partially consistent with previous studies in which another lncRNA (i.e.NEAT1) was found to regulate the development of AD by down-regulating micro-27a-3p 53 and targeting miR-107 in AD was found to exacerbate Aβ-induced neuronal damage. 54sides, we also discovered three new miRNA targets.Although KCNQ1OT1 has been reported to be up-regulated in in vitro matured (IVM) mouse offspring, 55 to the best of our knowledge, this is the first study reporting that HCG18 and KCNQ1OT1 act as ceRNA regulators in the pathogenesis of AD.However, the specific mechanisms by which they affect AD remains unclear.In this study, we discovered more miRNA targets of NEAT1 and XIST, providing more

| CON CLUS ION
This study identifies genes that are significantly associated with early AD and searched for miRNAs and lncRNAs targeted genes.Four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) were identified as potential biomarkers for AD.Therefore, inhibiting endogenous XIST, NEAT1, KCNQ1OT1 and HCG18 expression F I G U R E 4 Analysis of pivotal pathways and biological process of Early-DEGs and Cross-platform normalized expression level of seven hub genes in different brain regions.The PPI interaction network of differentially expressed genes constructed in STRING database (A).Red represents the hub genes that were differentially expressed in AD mouse models before the emergence of AD pathology (B).Biological process (C) and pathway analysis (D) of hub genes that were differentially expressed in AD mouse models before the emergence of AD pathology.The expression levels of seven hub genes (Atp6v0b, Atp6v0c, Atp6v0d1, Atp6v1e1, Atp6v1f, Slc17a7, andSyt1) in different brain regions (E-K).
TA B L E 4 Expression of seven hub genes in different brain regions.

F I G U R E 2
Identification of DEGs and functional enrichment analysis.Volcano plots showing the differentially expressed genes in AD and control samples of different brain regions.Blue dots represent significantly down-regulated genes in AD samples and red dots represent significantly up-regulated genes (A-D).Heatmap showing the expression levels of the top 20 DEGs in each dataset (E-H).Venn diagrams showing 29 DEGs (I) from datasets of 4 different brain regions.GO terms (J-M) and Top 20 enriched KEGG pathways (N-Q) are significantly associated with the expression of DEGs in 4 regions of the human AD brain.TA B L E 3 Hub genes in different brain regions.

F I G U R E 3
Analysis of key pathways and biological processes of hub genes in the PPI network.41 hub genes in the EC region (A); 29 hub genes in the HP region (B); 12 hub genes in the TC region (C); and 11 hub genes in the FC region (D).The lines indicate the interaction of genes.Biological process (E) and enriched Top 5 pathways (F) of hub genes in different brain regions.

| 9 of 12 HUANG
the known isoforms, the low-affinity vesicular synaptotagmin Syt1 functions in synchronized vesicle fusion.42Syt1 has also been shown to play a crucial role in the release of neurotransmitters in the et al.presynaptic terminal ganglia.43CFG analysis showed that Syt1 can interact with MAPT, which is the coding gene of AD core Tau protein that is expressed in most nerve cells.This demonstrates a potential association of Syt1 with the pathogenesis of AD.Besides, to the best of our knowledge, this is the first study reporting a potential role of Atp6v0b, Atp6v1e1 and Atp6v1f in AD.Benefiting from the relatively high stability of miRNA and its easy detection in body fluids, it has become an attractive biomarker for early AD.Studies have shown that miR-191, miR-103, miR-125b, miR-222 and miR-193b are dysregulated in blood samples of AD pa- insights for the diagnosis and treatment of early AD.Importantly, HCG18 and KCNQ1OT1 are potential candidate biomarkers for AD pathogenesis.Most of the relevant contemporary literature pertains to studies exploring how lncRNAs participate in the pathogenesis of AD as ceRNA after pathological changes in AD.Zhang et al.56 identified three lncRNAs (AP000265.1,KB-1460A1.5 and RP11-145M9.4) as participating in the occurrence of NFTs by competing with miRNAs for binding.lncRNAs TCONS_00367775, TCONS_00323331 and TCONS_00204925 regulate miRNAs closely associated with Aβ, thereby inhibiting the expression of related mRNAs.57The present study is the first study to analyse the early ceRNA network that may exist before the development of overt pathological changes of AD.The analysis of ceRNA regulatory network showed that ln-cRNA NEAT1, XIST, HCG18 and KCNQ1OT1 may competitively bind hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p and regulate Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 in AD brain.Our results deepen the understanding of the underlying mechanism by which these lncRNAs regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 and offer new ideas for the early prevention of AD.One of the limitations of the current study is that bioinformatics analysis was not validated by experimental techniques; their functions in causing AD require further investigation.

Construction of lncRNA-miRNA-mRNA competing endogenous RNA network for analysis of early-DEGs
Competing endogenous RNAs (ceRNAs) were predicted according to the hypothesis, and a lncRNA-miRNA-mRNA network related to early-DEGs of AD was constructed.First, the miRNA expression profile and sample details of GSE16759 were downloaded from the publicly available repository GEO database (https:// www.ncbi.nlm.nih.gov/geo/ ) to identify different expressions of miRNAs.Then, we used miRNet to predict lncRNAs.2.8 | Statistical analysisR software (Version 3.6.3)was used for statistical analysis.The differences of gene data between two groups were compared by independent t-test in 4 different brain regions.p-values <0.05 were considered indicative of statistical significance.